Bibliographic record
Abstract
Consider a seemingly fruitful ordering of our intellectual labours: think in ideal terms about justice and legitimacy, then bring our ideas and arguments to bear upon messy (and very much nonideal) real-world complications. This ordering is most often associated with John Rawls, but this was not his actual practice. That is no vice: Rawls took wide reflective equilibrium seriously as a philosophical method, moving back and forth from ideal to nonideal considerations in ways that belie the usefulness of any priority claims. We do not gain much insight by asserting the priority of either ideal or nonideal theorizing. Indeed, we could stop using those terms altogether, and theory might be none the worse, if we embrace something like Rawls’ constructivist method. Normative political theory should, then, reject certain methodological conceits lurking in some of the philosophical work we otherwise embrace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".